Learning from Demonstration or Imitation Learning [1]}, [2]}, [3]} and Reinforcement Learning [4]}, [5]} have proven to be the key techniques for learning sequential decision-making tasks through demonstration and experience respectively. Reinforcement Learning requires a hand-crafted reward signal which the agent tries to maximize over time and in turn learns the desired policy through trial-and-error. The agent has to learn to assign credit to past actions faithfully in case of delayed rewards and also balance exploration and exploitation effectively to arrive at an ideal policy, both of which are extremely challenging [6]}, [7]}. In Learning from Demonstration, on the other hand, the agent learns by directly trying to mimic the decisions of an expert demonstrator. Though originally designed for low-dimensional state-space tasks, with the advent of superior supervised function approximators [8]}, [9]}, [10]}, a lot of progress has been made in scaling these algorithms to very high dimensional state-space tasks enabling agents to learn policies directly from images [11]}, [12]}, [13]}, [14]}.
